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Remote Sensing for Land & Resources    2019, Vol. 31 Issue (3) : 87-94     DOI: 10.6046/gtzyyg.2019.03.12
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Research on the application of LBV transformation in domestic ZY-3 satellite images
Baoquan WEI1,2, Anning SUO2, Ying LI1, Jianhua ZHAO2
1. College of Navigation, Dalian Maritime University, Dalian 116026, China
2. National Marine Environmental Monitoring Center, Dalian 116023, China
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Abstract  

According to the spectral features of domestic ZY-3 remote sensing images, the formula of LBV transformation for ZY-3 is proposed and deduced, and the feasibility of improving the quality of ZY-3 remote sensing images is testified. At first, based on the characteristics of ZY-3 remote sensing images, the spectral information of nine types of typical ground features were selected, and regression coefficients were used to calculate regression coefficients. Then, the three components of L, B, V of ZY-3 satellite images were calculated according to the characteristics of the typical ground features space (bare land, water body, vegetation), color space (red, green, blue) and the space of LBV variables (the general radiance level of the ground objects, the visiable - infrared radiation balance, the band radiance variation vector). Finally, the experiments of ZY-3 remote sensing image in Ningde City of Fujian Province were carried out, and quantitative analysis was conducted to evaluate the experimental results. Firstly, the results show that, in the aspect of the visual effects, compared with the original image, the transformed image is more clear, and the details are more abundant, and thus can contribute more to the determination and identification of subsequent features. Secondly, through the LBV transformation, the image information entropy is 6.21, the average gradient is 4.71, the deviation coefficient is 0.46, and the quality of the remote sensing image is better than other transformation methods. Thirdly, by classifying the LBV image, the overall accuracy is up to 89.71%, and the Kappa coefficient is the highest, reaching 0.875 3. The classification accuracy is higher than that of other transformation methods. Therefore, The LBV transformation can improve the quality of ZY-3 remote sensing image, and it can be applied to ZY-3 remote sensing image processing and information extraction.

Keywords LBV transformation      image transformation      high spatial resolution      ZY-3 satellite      accuracy analysis     
:  TP79  
Issue Date: 30 August 2019
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Baoquan WEI
Anning SUO
Ying LI
Jianhua ZHAO
Cite this article:   
Baoquan WEI,Anning SUO,Ying LI, et al. Research on the application of LBV transformation in domestic ZY-3 satellite images[J]. Remote Sensing for Land & Resources, 2019, 31(3): 87-94.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.03.12     OR     https://www.gtzyyg.com/EN/Y2019/V31/I3/87
地点 经度 纬度 日期 地点 经度 纬度 日期 地点 经度 纬度 日期
大连 E123.2° N39.4° 20160616 沧州 E119.2° N39.8° 20160625 舟山 E122.6° N30.3° 20170828
盘锦 E120.9° N40.6° 20170704 天津 E118.2° N39.4° 20170604 泉州 E118.8° N24.7° 20160727
唐山 E119.0° N39.0° 20160625 南通 E120.9° N32.7° 20170803 莆田 E119.2° N25.4° 20170712
滨州 E118.7° N37.8° 20160625 连云港 E119.3° N34.6° 20170901 珠海 E113.7° N22.3° 20161016
青岛 E119.7° N35.4° 20160517 宁波 E121.7° N29.1° 20160621 汕头 E116.4° N23.5° 20171025
湛江 E110.9° N21.5° 20170821 防城港 E108.5° N21.5° 20160921 北海 E109.7° N21.5° 20160709
三亚 E109.7° N18.5° 20160606 海口 E110.2° N19.9° 20160709 文昌 E111.0° N19.6° 20170831
Tab.1  Information of ZY-3 images
Fig.1  Spectral curves of nine ground features of ZY-3
Fig.2  Flow chart of LBV
Fig.3  Quadratic regression curves of nine grourd features
Fig.4  Linear regression curves of nine ground features
Fig.5  Quadratic regression curves and residuals of building and forest
Fig.6  LBV transformation images
方法 信息熵 平均梯度 偏差系数
原始影像 5.32 3.69
Brovery 3.72 2.87 2.14
PCA 5.11 3.37 1.24
G-S 5.54 3.84 1.03
CNSS 5.42 3.37 0.44
Pan-sharpening 6.18 4.35 0.96
本文方法 6.21 4.71 0.46
Tab.2  Evaluation indexes of ZY-3 image
方法 最大似然法 支持向量机 神经网络
总体精
度/%
Kappa
系数
总体精
度/%
Kappa
系数
总体精
度/%
Kappa
系数
原始影像 68.42 0.579 2 72.40 0.630 2 71.12 0.621 0
Brovery 71.10 0.620 5 73.57 0.651 3 73.47 0.648 1
PCA 76.05 0.673 7 79.30 0.743 4 80.19 0.746 8
G-S 74.21 0.664 2 77.56 0.727 7 84.76 0.821 6
CNSS 78.30 0.734 1 82.45 0.778 0 83.10 0.805 7
Pan-shar-pening 81.39 0.751 0 86.87 0.840 5 87.06 0.853 4
本文方法 84.47 0.816 3 88.76 0.866 7 89.71 0.875 3
Tab.3  Classification accuracy of ZY-3 image
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